Multi-Task Label Embedding for Text Classification

Zhang, Honglun; Xiao, Liqiang; Chen, Wenqing; Wang, Yongkun; Jin, Yaohui

doi:10.18653/v1/d18-1484

Cited by 69 publications

(20 citation statements)

References 17 publications

(34 reference statements)

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“…In the field of NLP, Tang et al studied the applications of label embeddings for text classification in the heterogeneous networks [33]. Furthermore, multitask learning also fully utilized label embeddings [34], which is beneficial to experimental results. At present, more and more researchers devote themselves to improve the performance of related tasks.…”

Section: Label Embeddingmentioning

confidence: 99%

A Fusion Model-Based Label Embedding and Self-Interaction Attention for Text Classification

et al. 2020

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Text classification is a pivotal task in NLP (Natural Language Processing), which has received widespread attention recently. Most of the existing methods leverage the power of deep learning to improve the performance of models. However, these models ignore the interaction information between all the sentences in a text when generating the current text representation, which results in a partial semantics loss. Labels play a central role in text classification. And the attention learned from text-label in the joint space of labels and words is not leveraged, leaving enough room for further improvement. In this paper, we propose a text classification method based on Self-Interaction attention mechanism and label embedding. Firstly, our method introduce BERT (Bidirectional Encoder Representation from Transformers) to extract text features. Then Self-Interaction attention mechanism is employed to obtain text representations containing more comprehensive semantics. Moreover, we focus on the embedding of labels and words in the joint space to achieve the dual-label embedding, which further leverages the attention learned from text-label. Finally, the texts are classified by the classifier according to the weighted labels representations. The experimental results show that our method outperforms other state-of-the-art methods in terms of classification accuracy. INDEX TERMS Text classification, text representations, label embedding.

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Section: Label Embeddingmentioning

confidence: 99%

A Fusion Model-Based Label Embedding and Self-Interaction Attention for Text Classification

et al. 2020

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“…The SSTb corpus was utilized for the first time by Pang et al [47] for Sentiment Analysis research. The extension of this SSTb as a benchmark dataset was used by Sochar et al [48].…”

Section: • Stanford Sentiment Treebank (Sstb)mentioning

confidence: 99%

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Ghosh¹,

Sanyal²

2020

Preprint

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Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.

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“…We tested our baseline on the whole data set, and it performed similarly (95.3%), which serves as a validation of the baseline approach used in this study. Next, the accuracy on the Reuters data set was recently reported to be 80-85%, where multi-objective label encoders were used [54]. Our baseline implementation performs with 75% accuracy.…”

Section: Resultsmentioning

confidence: 99%

Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture

Škrlj

Kralj

Lavrač

et al. 2019

MAKE

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Deep neural networks are becoming ubiquitous in text mining and natural language processing, but semantic resources, such as taxonomies and ontologies, are yet to be fully exploited in a deep learning setting. This paper presents an efficient semantic text mining approach, which converts semantic information related to a given set of documents into a set of novel features that are used for learning. The proposed Semantics-aware Recurrent deep Neural Architecture (SRNA) enables the system to learn simultaneously from the semantic vectors and from the raw text documents. We test the effectiveness of the approach on three text classification tasks: news topic categorization, sentiment analysis and gender profiling. The experiments show that the proposed approach outperforms the approach without semantic knowledge, with highest accuracy gain (up to 10%) achieved on short document fragments.

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Multi-Task Label Embedding for Text Classification

Cited by 69 publications

References 17 publications

A Fusion Model-Based Label Embedding and Self-Interaction Attention for Text Classification

A Fusion Model-Based Label Embedding and Self-Interaction Attention for Text Classification

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture

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